使用视觉-语言模型检测和缓解多模态模因中的仇恨内容
Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models
AI总结：
本文利用视觉-语言模型（VLMs）解决多模态模因中仇恨内容的检测与缓解问题，提出定义引导提示技术和统一框架UnHateMeme，通过替换仇恨组件将其转化为非仇恨形式，并在LLaVA等模型上验证了有效性。
摘要：
社交媒体的快速发展为个人创建在线内容提供了增强的通信渠道，使他们能够表达自己的想法和观点。多模态模因通常利用视觉和文本元素进行戏谑或幽默的表达，但有时会被滥用来传播针对个人或群体的仇恨言论。虽然仇恨模因的检测已被充分研究，但开发有效方法来转化模因中的仇恨内容仍然是一个重大挑战。利用视觉-语言模型（VLMs）强大的生成和推理能力，我们解决了检测和缓解仇恨内容的任务。本文提出了两项关键贡献：首先，一种用于检测仇恨模因的定义引导提示技术；其次，一个用于缓解模因中仇恨内容的统一框架，名为UnHateMeme，该框架通过替换仇恨文本和/或视觉组件来工作。通过我们的定义引导提示，VLMs在仇恨模因检测任务上取得了令人印象深刻的性能。此外，我们的UnHateMeme框架与VLMs集成，展示了将仇恨模因转化为非仇恨形式的强大能力，这些形式符合人类水平的仇恨言论标准，并保持图像和文本之间的多模态连贯性。通过实证实验，我们展示了LLaVA、Gemini和GPT-4o等最先进的预训练VLMs在所提出任务上的有效性，并对它们在这些任务上的各自优势和局限性提供了全面分析。本文旨在阐明VLMs在确保安全和尊重的在线环境方面的重要应用。
英文摘要：
The rapid evolution of social media has provided enhanced communication channels for individuals to create online content, enabling them to express their thoughts and opinions. Multimodal memes, often utilized for playful or humorous expressions with visual and textual elements, are sometimes misused to disseminate hate speech against individuals or groups. While the detection of hateful memes is well-researched, developing effective methods to transform hateful content in memes remains a significant challenge. Leveraging the powerful generation and reasoning capabilities of Vision-Language Models (VLMs), we address the tasks of detecting and mitigating hateful content. This paper presents two key contributions: first, a definition-guided prompting technique for detecting hateful memes, and second, a unified framework for mitigating hateful content in memes, named UnHateMeme, which works by replacing hateful textual and/or visual components. With our definition-guided prompts, VLMs achieve impressive performance on hateful memes detection task. Furthermore, our UnHateMeme framework, integrated with VLMs, demonstrates a strong capability to convert hateful memes into non-hateful forms that meet human-level criteria for hate speech and maintain multimodal coherence between image and text. Through empirical experiments, we show the effectiveness of state-of-the-art pretrained VLMs such as LLaVA, Gemini and GPT-4o on the proposed tasks, providing a comprehensive analysis of their respective strengths and limitations for these tasks. This paper aims to shed light on important applications of VLMs for ensuring safe and respectful online environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
